Method for forming a cohort for use in identification of an individual

ABSTRACT

A method of forming a cohort for use in identification of an individual by comparing a model of characteristics of the individual, such as a model of utterances, with models of the cohort including a model for the client in respect of whom it is desired to test whether the individual is identifiable. Models related to the population excluding the client are tested to determine whether they meet an acceptance threshold test as to identify with a model for the client. Then, from each meeting the threshold test, it is determined whether those models are distributed so as to present at least a substantial probability that models for nonmembers of the population spaced from the client model in all directions will each be closer to a member of the cohort, excluding the client, than to the client. If that probability is less than a predetermined value, a selection is made from the population of another cohort member which will reduce that probability. Alternatively, if the mentioned probability is less than the predetermined value, a &#34;phantom&#34; model is generated for inclusion in the population and which will reduce that probability. The method may employ both the described selection and &#34;phantom&#34; generation techniques.

TECHNICAL FIELD

This invention relates to a method for forming a cohort for use inidentification of an individual, and to a method of identification of anindividual on the basis of that cohort. The method is concernedprimarily, but not exclusively, with forming a cohort for use inidentification of individuals on the basis of the degree of conformityof characteristics of voice sounds, but may be applied to identificationon the basis of other characteristics of individuals.

BACKGROUND OF THE INVENTION

In determining whether an individual is or is not a particularpre-identified individual ie a "client", comparison may be made asbetween pre-determined parameters relating to the pre-determined personand those measured when any individual is presented for verification.Particular parameters which may be used include parameters relating tospeech, although parameters relating to other characteristics may beused. Among those other characteristics are parameters relating to howthe presenting individual writes, uses a computer mouse, or uses acomputer or other keyboard.

One method of identification, or verification, of whether or not anindividual presenting for verification is or is not a pre-determinedindividual makes use of client models representing each of a populationof individuals. Characteristics relating to a person presenting forverification are measured and compared with the characteristics for oneor more of the total population. If the characteristics for the personpresenting for verification match those for a particular one of thepopulation, then the verification system makes a determination that thepresenting person is the particular individual for which thecharacteristics match. A difficulty with systems of this kind is thatvalues for characteristics for any person presenting may differ fromreference values for that person which are used by the system. Forexample, the values for characteristics used by the system wouldnormally comprise stored values measured in a previous test on theindividual, the stored value then being compared with those measuredwhen the person presents for verification. However, naturally occurringvariations may exist as between those values stored and those whicharise when a verification procedure is carried out. In the case ofverification on the basis of characteristics relating to utterances of aperson, those variations may, for example, comprise phonetic variations,variations due to environmental conditions and intra speaker variations.Thus, a person may utter a vowel in one fashion when the vowel appearsin one word, and in a different fashion when it appears in another word.Again, the test conditions under which the original characteristicvalues were determined may be noise free, but there may be noise presentin the environment when the individual presents for verification.Generally, the, it is not surely possible to effect identificationsimply on the basis of direct equatability of measured characteristicswith those stored for the individual in question. Normally, comparisonis effected as between characteristic values for more than one of thepopulation, the determination of identity being made on the basis of the"distance" between the characteristics as stored for more than one ofthe population and those measured at verification. The characteristicswhich are measured in the verification process may be multi dimensional.For example, it has been found convenient to use cepstral analysistechniques to analyse the speech of a population and the personpresenting for verification. Overlapping samples of, say, 30 millisecondmay be taken of the amplitude-time wave form recorded during speech. Inthis case, it is convenient to generate 15 cepstral coefficients and togenerate a model representing each member of the population and of theperson presenting for verification, the models being 15 dimensional andwith, for example, 128 points. The set of such points is commonlyreferred to as a code book for the person in question.

In the comparison of the code book of the person presenting forverification and those for the reference population employed by theverification technique, one may choose from the code books for thepopulation code books of a "cohort", being a limited number of thepopulation, and then compare the code book of the presenting person withcodes books for that cohort. The cohort is selected from the totalpopulation on the basis that there is some similarity between the codebook for the "client" in the population (ie the person whom the personpresenting for verification purports to be) and the relevant cohortmembers. Selection of the cohort members can be made on the basis of theproximity of the centroids of the code book distributions to thecentroid of the client's code book distributions. It is important thatthe multi-dimensional (Euclidean) distance between the centroid for theclient and the various cohort members be significant, but not too great.

While methods based on the above have been found to be workable,hitherto inexplicable errors sometimes arise. For example, an error asbasic as failure to discriminate between a male and a female voice mayoccur. It has now been determined that a likely cause of this difficultyis that the cohorts which are selected for the particular client do nothave code book distributions which "surround" the code bookdistributions for the client in a satisfactory fashion. In particular,if the distance from the centroid of the code book distributions for theperson presenting for verification to the client code book distributioncentroid is great, then the difference between the distance to thecentroids of the code book distributions for the client and for othercohort members will be relatively small. It may easily arise in thiscase that, because of the distribution of the cohort members withrespect to the client, the distance between the code book distributioncentroids of the client and of the person presenting for verification isless than the distance from the code book distributions centroid for theperson presenting for verification than any of the other cohort members,at least as applies to some particular direction as between the codebook distribution centroids for the person presently for verificationand for the client and cohorts. Thus, the verification scheme mayincorrectly assume that the person presenting for verification is theclient in this instance. Merely increasing the number of cohorts willnot necessarily rectify this problem.

SUMMARY OF THE INVENTION

In accordance with the present invention, the "coverage" is extended by

a) selecting appropriate new cohort members from the population, and/or

b) generating from data relating to existing cohort members, includingor excluding a particular client, a model for inclusion in the cohort.

More particularly, embodiments of the invention provide methods forsynthesising speech models for "phantom" speakers with specified speechcharacteristics, comprising:

(i) for determining the desired characteristics for each successivecohort member during incremental assembly of a cohort; and/or

(ii) constructing synthetic speech models with the desiredcharacteristics.

The synthesised models may be formed from combinations of real speechmodels. For example, speech events fall into several different classes(volcalic, fricative, nasal, etc.); during the synthesis procedure,those parts of the real speech models pertaining to different classes ofspeech events may be considered separately. As a result of their methodof composition, the synthesised speech models may be representative ofpossible real speakers.

In one specific aspect, the invention comprises a method of assembling acohort for a client being one of a population, comprising testingwhether models of at least a substantial number (preferably all) of thepopulation excluding the client meet an acceptance threshold test as toidentity with a model for the client, determining, from each modelmeeting the threshold test, whether those models are distributed so asto present at least a substantial probability that models fornon-members of the population spaced from the client model in alldirections will each be closer to a member of the cohort, excluding theclient, than to the client and, if that probability is less than apredetermined value, selecting from the population another cohort memberwhich will reduce that probability.

In another aspect, the invention provides a method of assembling acohort for a client, being one of a population, comprising testingwhether models of at least a substantial number (preferably all) of thepopulation excluding the client meet an acceptance threshold test as toidentity with a model for the client, determining, from each modelmeeting the threshold test, whether those models are distributed so asto present at least a substantial probability that models fornon-members of the population spaced from the client model in alldirections will each be closer to a member of the cohort, excluding theclient, than to the client and, if that probability is less than apredetermined value, generating a new model for inclusion in thepopulation and which will reduce that probability.

In another aspect the invention provides a method of assembling a cohortfor a client, being one of a population, comprising testing whethermodels of at least a substantial number (preferably all) of thepopulation excluding the client meet an acceptance threshold test as toidentify with a model for the client, determining, from the or eachmeeting the threshold test, whether those models are distributed so asto present at least a substantial probability that models fornon-members of the population spaced from the client model in alldirections will each be closer to a member of the cohort, excluding theclient, than to the client and, if that probability is less than apredetermined value, either selecting from the population another cohortmember which will reduce that probability or generating a new model forinclusion in the population and which will reduce that probability.

The invention also provides a method of verification using a cohortassembled as above described.

The invention may be practiced with models of different types, forexample vector quantisation or hidden Markov models.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a graph plot illustrating analysis of residuals with similarand dissimilar codebooks.

FIG. 2 is a diagram depicting a hyperellipsoid concentric with a clientcontaining centroids of codebooks for speakers similar to the client,

FIG. 3 is a table illustrating Achieved Equal Error Rate percentages.

FIGS. 4A, 4B, and 4C form a listing of a "C" computer program suitablefor finding the average vector distance between sets of paired cohorts.

FIGS. 5A, 5B, and 5C form a listing of a "C" computer program suitablefor synthesizing codebook distributions for a "phantom" populationmember.

DETAILED DESCRIPTION OF THE INVENTION

The following detailed description describes in more detail the contextof the invention, and preferred features of the invention.

The "cohort normalised" method of speaker verification computes for eachinput utterance its relative distance from models of the client and acohort of speakers drawn from the same population. It is assumed thatvariations which reduce the utterance fit to the client model will tendto have similar effects with respect to the cohort speaker models. Theuse of "relative distance" can lead to improved client/impostordiscrimination.

The following relates to the design of suitable cohorts. Using VQcodebooks in multidimensional cepstral space as the basic speakermodels, pairs of codebooks can be related geometrically in terms ofvector differences between their centroids in cepstral space. In awell-designed cohort, the cohort members give adequate "coverage" of theclient's codebook in multidimensional space.

Cohort members are usually chosen on the basis of their similarity tothe client. Experiments in which cohort members were instead chosenaccording to their position relative to the client led to a slightimprovement in verification performance, suggesting that jointconsideration of similarity and position would give even better results.However, with a limited set of speakers, it will often be difficult tofind cohort members who meet these simultaneous requirements. At leastin certain cases it is possible to synthesise suitable "phantom"codebooks based on those of real speakers.

In the classic procedure for speaker verification, an input utterance isaccepted or rejected according to a threshold on its goodness of fitwith a model of the client's speech. While such a measure truly reflectsabsolute deviations between the client's model and input utterances, itis sensitive to overlapping client and impostor distributions whicharise because of the effects of intra-speaker variation, recordingenvironment change and phonetic variation. This in turn leads to a highEqual Error Rate (EER).

An alternative approach (Rosenberg et al., 1992) uses a "cohort" ofspeakers, with speech models similar to that of the client, allowingrelative measures of similarity or difference to be computed andreducing problems due to the above-mentioned variations. Similarity isjudged on the basis of the mean distortion of a potential cohortspeaker's utterances with respect to the client speaker's VQ model.

Tests of the cohort method show that it is subject to problems withfalse acceptance of impostor utterances which are quite dissimilar tothose of the client (eg. from a speaker of opposite sex to the client)but which still give a better fit to the client model than to any of thecohort models. A tentative geometrical explanation of this problem hasbeen given in Chen, F., Millar, B. and Wagner, M. (1994), "Hybridthreshold approach in text-independent speaker verification," Proc. Int.Conf. on Spoken Language Processing, Yokohama, 1855-1858, suggestingthat the problem arises from inadequate "coverage" of the client bycohort members. Thus, a significant practical difficulty associated withuse of the cohort-normalised method is that of assembling a suitablecohort from among the set of individuals whose speech has been modelled.In many cases, this set will be too small and for certain clients willnot include a suitable set of speakers with similar speech models fromwhich to assemble a cohort. Choice of suitable cohort members needs tobe based on an understanding of the relationship between pairs ofcodebooks. Unless suitable potential cohort members are available andthe cohort members are selected carefully, anomalous verificationbehaviour may result (e.g. an impostor of the opposite sex beingverified as the client). Verification performance tends to improve withcohort size, but this increases verification time. By appropriate choiceof cohort members, one can form a cohort of minimum size for a specifiedlevel of performance.

The techniques covered of the present invention directly addresspractical difficulties associated with assembling a suitable cohort foreach client in the absence of a large set of speech models from which toselect cohort members. Speakers may, in the following, be considered tobe characterised by codebooks of 128 codewords (vectors in 15-Dmel-frequency cepstral space) chosen such as to minimise the encodingerror (distortion) with the training data sets. The number of musclegroups used in articulating speech sounds is much less than 15. Most ofthe relevant information for phonetic discrimination in the speech oftwo males can be represented with about six cepstral coefficients Davis,S. B. and Mermelstein, P. (1980), "Comparison of parametricrepresentations for monosyllabic word recognition in continuously spokensentences," IEEE Trans. Acoustics, Speech and Signal Processing, Vol.ASSP-28, 357-366. High-dimensional cepstral data relating to vocalicspeech tends to fall on low-dimensional quadratic surfaces(predominantly parabolic) which can be characterised in terms of onlyfour parameters Hawkins, S., Macleod, I. and Millar, B. (1994),"Modelling individual speaker characteristics by describing a speaker'svowel distribution in articulatory, cepstral and formant space," Proc.Int. Conf. on Speech Science and Technology, Perth. Important componentsof the codeword distributions will thus have lower intrinsicdimensionality than that of their space of representation; the overalldistributions can thus be expected to show significant clustering incepstral space.

The similarity of a pair of codebooks may be assessed by measuring thedistortion when one codebook is used to encode the speech data on whichit was trained, and then to compare this to the distortion obtained withthis data using the other codebook. Given that the codebooks for allspeakers have been trained on the same set of utterances, the regionsmost densely occupied by codewords should be similar for pairs ofsimilar codebooks.

The similarity of codebooks measured in such a way represents thesimilarity of speakers. The ratio of distortions is a scalar magnitude;as a directionless quantity it thus gives no indication as to which oftwo given codebooks would yield the smaller distortion when encoding thetraining data for a third, for example. As a similarity measure itprovides an estimate of how "close" the regions of cepstral spaceoccupied by the two codebooks are, but it does not indicate theirrelative positions. Scalar measures are thus of only limited use indiagnosing problems with a given cohort or in choosing cohort membersfor a given client.

A simple vector measure considers the relative differences betweencodebook centroids (formed from the average of all vectors in acodebook). While pairs of speaker models which give relatively smallerrors in encoding each other's training data will have similardistributions of codewords in cepstral space, with pairs of similarcodebooks there may still be considerable interspersion of codewords.The question arises as to whether any differences (in magnitude anddirection) between the centroids of such codebooks are meaningful in thestatistical sense. Given the inhomogeneity and complexity of thecodeword distributions in cepstral space, simple statisticalcharacterisations (based on variances of these distributions) are notappropriate for answering this question. An alternative methodassociates the codewords in one book with neighbours in the other (eg.on the basis of closest Euclidean distance, as used in the following)and then analyses the distributional properties of the resulting set of128 difference vectors, to see if they cluster in particular directions.A method for analysing these properties is next described.

A method to test the statistical significance of vectorial relationshipsbetween codebooks is now developed. The analysis of the differencevectors comprises the following steps:

(i) determine a mean directional component;

(ii) test the statistical significance of this component; and

(iii) subtract the mean from all vectors before analysing the residualswith Principal Components Analysis (PCA).

The mean vector between codeword pairs can simply be shown to be equalto the vector between the corresponding codebook centroids. PCA is usedto check to what extent directional variability between codeword pairsis concentrated in a few directions.

Distributions of difference vectors with relatively similar anddissimilar pairs of codebooks have been analyses in accordance with theprinciples of this invention (with similarity being assessed in terms ofaverage distortion), using Hotelling's T² statistic to test thehypothesis that the mean vector of the difference vectors was non-zero.For all pairs of codebooks examined, the hypothesis was confirmed(p<0.0001) showing that the difference vectors tend to point in aconsistent direction. As a result of the low intrinsic dimensionality ofthe cepstral distributions of vocalic speech (Hawkins, Macleod andMillar, 1994), a significant proportion of the codewords will tend tocluster on hypersurfaces of lower dimensionality. If, however, there wassubstantial interspersion of the codeword distributions in the codebooksbeing compared, the difference vectors would have a less consistentorientation. The results of the analysis performed in this embodiment ofthe invention show that the degree of interspersion is limited, thusindicating that distributions of codewords from similar codebooks havesimilar shapes and that the concept of relative displacements betweencodebook pairs has statistical validity. After subtracting the meanvector from each difference vector, analysis of the residuals with PCArevealed one distinct non-noise directional component with dissimilarpairs of codebooks and two orthogonal components with similar codebookpairs (one component being somewhat larger than the other). This isshown in FIG. 1 which illustrates analysis of residuals with similar anddissimilar codebooks.

The presence of non-noise Principal Components in the residuals, afterthe mean vector is subtracted, means that there are further systematicvariations in the relationships between pairs of codeword distributionsin addition to the mean displacement. Two codebooks with similarcentroids may thus give large distortions when encoding each other'straining data (eg. if one codebook had a greater span in certaindirections than the other).

An estimate of progress towards explaining the total relationshipbetween two codebooks is obtainable by computing the length of the(vector) sum of the difference vectors and comparing this to the sum ofthe scalar lengths of the individual vectors. If all difference vectorspoint in the same direction, these two lengths will be the same. If thedifference vectors are randomly oriented, the summed vector length willbe only a small fraction of the scalar sum of lengths. On examining thecodebooks of potential cohort members in relation to given clientcodebooks, it was found that this length ratio varied from about 25% to40%, a much larger than expected length for the sum of random vectors.In addition to supporting the statistical finding that the differencebetween codebook centroids is real, this result means that a largeenough component of the total relationship is captured that clearbenefits should follow from taking relative codebook positions intoaccount when constructing cohorts.

The above provides statistical justification for using relative centroidpositions to consider the extent to which the members of a cohort"enclose" a client or leave "gaps" in the coverage, given possibleinterspersion of the codeword distributions of similar speakers. Theminimum distortion among the cohort models and the mean distortionacross cohort models have both been proposed for use in theclient/cohort comparison. The following tests are based on use of themin statistic.

An optimal cohort is one in which for each potential impostor there is acohort member whose codebook encodes impostor utterances with lowerdistortion than that achieved with the client's codebook. Care is needednot to falsely reject the client's speech, so such cohort codebooks needto encode the client's training data with a significantly (but notdramatically) larger distortion than that obtained with the client'scodebook: The cohort members should be similar, but not too similar, tothe client. A percentage of impostors with speech very similar to theclient will thus be falsely accepted, but this is unavoidable. Referringto FIG. 2, imagine a hyperellipsoid (concentric with the client), whichcontains the centroids of codebooks for speakers similar to the client.The members of one potential cohort could then be distributed on thesurface of a second larger hyperellipsoid with roughly twice thediameters of the first, so that (on average) utterances made by speakerswhose codebook centroids lay outside the first hyperellipsoid would beattributed to a cohort member, and utterances made by speakers whosecodebook centroids lay inside would be attributed to the client. Byvarying the size of the smaller hyperellipsoid, achieve the desiredbalance between Type I and Type II errors can be achieved.(Hyperellipsoids are advanced here instead of hyperspheres, because ofthe fact that other codebooks are unlikely to be evenly distributedabout the client's.)

In the usual case, only a limited set of speakers (and their trainedcodebooks) will be available for cohort construction. The most similarspeakers in this set to a given client may well be less (or sometimesmore) similar than desired. Nevertheless, a functional cohort of size Ncan be formed by choosing the N most similar codebooks. Just as thecodeword distributions themselves will be of lower intrinsicdimensionality than that of the representation space, it might beexpected that the relative positions of codebook centroids (and thus ofcohort members) will also be unevenly distributed. For example, cepstralfeatures will tend to vary in a systematic manner with changes inparameters such as vocal tract length and shape.

In terms of geometric analogy, anomalous acceptance of dissimilarimpostors with a cohort chosen from the speakers most similar to theclient arises because the client is "covered" too sparsely or toounevenly. An alternative procedure for assembling a cohort is asfollows. Choose a speaker who is similar (but not too similar) to theclient as the first cohort member. Test the remaining speaker populationto see which speaker (of about the desired similarity to the client)gives the highest percentage of false acceptances with this cohort ofsize one. This speaker will lie in a direction which is not well coveredby the first cohort member and is chosen as the second cohort member.The procedure is repeated until a cohort of the required size has beenformed.

Speech data useful in practicing the invention is described in Millar,B., Chen, F., Macleod, I., Ran, S., Tang, H., Wagner, M. and Zhu, X.(1994), "Overview of speaker verification studies towards technology forrobust user-conscious secure transactions," Proc. Int. Conf. on SpeechScience and Technology, Perth. The population of 45 speakers is dividedinto two--a cohort formation population of 25 speakers and a client/testpopulation of 20 speakers (10 male and 10 female). Using the method ofassessment outlined in Millar, B., Chen, F. and Wagner, M. (1994), "Theefficacy of cohort normalisation in a speaker verification task underdifferent types of speech signal variance," Proc. Int. Conf. on SpeechScience and Technology, Perth, a test was made of the verificationperformance of cohorts assembled (i) from the speakers most similar tothe client, and (ii) by starting with the most similar speaker to theclient, adding the speaker who gave the greatest number of falseacceptances with this cohort of size one, and so on as each new memberwas added. Because of the limited speaker population available, thesimilarity of cohort members to the client was not considered inbuilding up the cohort using the "optimum direction" method (which wasintended to identify and then fill gaps in the cohort coverage of theclient). The results given in Table 1 show a slight advantage for thedirection method, even though (apart from the first cohort member)similarity to the client was not considered. For several clients, theEER with the "optimum direction" procedure increased slightly as thecohort size increased from three to five; in this case the final one ortwo cohort members chosen must have led to false rejections of theclient (ie. these members were too similar to the client).

Analysis of the EERs achieved with Min5 and Sel5 showed that theobserved improvement with Sel5 was not statistically significant. Thusthese experiments indicate that the direction and similarity methodsproduce cohorts of similar quality. Given the different basis of thesetwo methods of assembling cohorts, simultaneous consideration of bothcoverage and similarity may improve overall performance.

Given the difficulties encountered with locating suitable cohort members(because of the limited population of speakers), the question arises asto whether it is possible to form synthetic codebooks with the desiredproperties. For example, it would be possible to modify the client'scodebook to get a new codebook which is just sufficiently dissimilar(ie. gives the desired amount of distortion when encoding the client'straining data with respect to the balance of Type I and Type II errors).For example, it would be possible to disturb 1 or more of the 15coefficients in each codeword at a time to yield synthetic cohortsdisplaced a desired distance from the client in the direction of thealtered coefficients. Experiments showed that codebooks synthesised inthis manner had little practical utility--they usually did not encodeimpostor utterances as efficiently as the client's codebook and thus didnot lead to improvements in speaker verification performance. The sourceof the problem here is the use of codeword distributions which are mostlikely densely clustered in only a small region of the 15-D cepstralspace. In synthesising "phantom" codebooks we need to ensure that thesynthetic codewords are representative of those of typical speakerssimilar to the client. Working in a space which is known to beinhomogeneously occupied, we can minimise errors arising frominhomogeneities by using codeword pairs from similar real speakers andinterpolating synthesised values, thereby staying "close" to known realvalues.

Experiments in synthesising codebooks by either adding or subtracting afixed vector displacement to or from all codewords in a real speaker'scodebook, either the client's or a (potential) cohort member's, wereinstructive. The fixed displacement was usually 50% of the differencevector between the client's and cohort's codebook centroids. In atypical example, the client's codebook encoded a set of test clientutterances with a distortion of 2783, the cohort's codebook gave adistortion of 3323, the client's codebook displaced by either + or -50%of the difference vector between the centroids gave distortions of 2811and 2799 respectively, and the cohort's codebook displaced by + or -50%of this difference vector gave distortions of 3422 and 3255respectively. Two points to be noted here are that (i) the observedincreases and decreases in distortion are consistent with our geometricinterpretation, and (ii) when the client codebook is displaced halfwaytowards the cohort, the distortion increases but is still substantiallysmaller than the (reduced) distortion obtained when the cohort codebookis displaced halfway towards the client.

The second point above provides further evidence that the distributionsof codewords vary in ways other than overall position--speakers arecharacterised by the shapes of their codeword distributions as well. Asecond method of interpolation was thus tried, which aimed to indirectlycapture something of these other dimensions of variation. Instead ofadding a fixed vector displacement to all codewords, interpolation (orextrapolation) was affected on the basis of individual differencevectors between codeword pairs. As an increasing percentage of thesedifference vectors are added to the codewords in the client codebook, sothe synthesised codebook will gradually change from one that is similarto the client codebook into one that is similar to the cohort codebook.For the example client and cohort codebooks considered above, asynthetic codebook interpolated using 50% of the individual differencevectors for codeword pairs gave a distortion of 3078, which was close tohalfway (3053) between the respective client and cohort distortions of2784 and 3323.

FIG. 3 illustrates Achieved Equal Error Rate percentages with anabsolute threshold (ABS₋₋ VQ) and with selected cohorts of size n chosenconventionally (Minn) and according to false acceptances (Seln). Thefinal column (Sel5') shows the improved results obtained with severalclients (marked with *) through use of a final synthetic cohort member.This demonstrates that with some clients the EER increased from Sel4 toSel5. In these cases, the chosen fifth cohort member was used toconstruct an extrapolated synthetic codebook (moving the chosen cohortcodebook further away from the client) and recalculated the EER (shownas Sel5'). In all cases this procedure prevented the EER from increasingbetween Sel4 and Sel5'; in two cases (clients 19 and 20) the syntheticcohort member reduced the EER between Sel4 and Sel5'. The reduction inthe overall error rate to 2.83% was not, however, sufficient to make thedifference between Min5 and Sel5' statistically significant.

The overall results of the experiments provide evidence that thedistributions of codewords in 15-D MFCC space are rather complex.Although it can be shown statistically that the observed meandisplacements between similar codebooks are real and do not occur justby chance, the distributions of codewords in given codebooks will varyin shape and extent as well as position. The present concept of relativecodebook positions captures an important part, but only a part, of thetotal relationship between similar codebooks.

The listing shown in FIGS. 4A-4C is for a "C" computer program suitablefor finding the average vector distance between sets of paired cohorts.

The program listing shown in FIGS. 5A-5C is for a "C" program suitablefor synthesizing codebook distributions for a "phantom" populationmember.

We claim:
 1. A method of assembling a cohort for a client being one of apopulation, comprising testing whether models related to the populationexcluding the client meet an acceptance threshold test as to identitywith a model for the client, determining, from each model meeting thethreshold test, whether those models are distributed so as to present atleast a given probability that models for non-members of the populationspaced from the client model in all directions will each be closer to amember of the cohort, excluding the client, than to the client and, ifthat probability is less than a predetermined value, selecting from thepopulation another cohort member which will reduce that probability. 2.The method as claimed in claim 1 wherein said models related to thepopulation excluding the client comprises all of the populationexcluding the client.
 3. The method as claimed in claim 1 wherein saidmodels are codebooks each of a number of codewords.
 4. The method asclaimed in claim 3 wherein said testing is effected by assessing adistance between centroids of pairs of the codebooks.
 5. The method asclaimed in claim 3 wherein said testing is effected by assessing adistance between codewords in one said codebook and neighbour codewordsin another said codebook.
 6. The method as claimed in claim 4 whereinthe distance is a Euclidean distance.
 7. The method of claim 5 whereinthe distance is a Euclidean distance.
 8. The method of claim 1, furthercomprising:(a) choosing a first model among models of the population notincluding the client model, said first model being similar to but stillexhibiting significant differences with respect to the client model, (b)adopting said test model as a first member of the cohort, (c) testingthe remaining models for the population, excluding the client and firstmodels, to determine a further model, among those of the remainingmodels which have a degree of similarity to the client model similar tothat which exists between the first and client models, which providesthe highest degree of false acceptances with respect to the client, (d)adding said further model to said cohort, and (e) repeating steps (c)and (d) using all models previously added to the cohort and the clientmodel to generate successive other further models which are added to thecohort.
 9. The method of claim 1 wherein the models are vectorquantization or hidden Markov models.
 10. The method of claim 1 whereinsaid models represent speech characteristics.
 11. The method of claim 1,further comprising comparing a model relating to said person with saidcohort and determining whether the person is the client on the basis ofsimilarity of the models relating to the person and to the cohort. 12.The method of assembling a cohort for a client being one of apopulation, comprising testing whether models related to the populationexcluding the client meet an acceptance threshold test as to identitywith a model for the client, determining, from each model meeting thethreshold test, whether those models are distributed so as to present atleast a given probability that models for non-members of the populationspaced from the client model in all directions will each be closer to amember of the cohort, excluding the client, than to the client and, ifthat probability is less than a predetermined value, generating a newmodel for inclusion in the population and which will reduce thatprobability.
 13. The method as claimed in claim 12 wherein said modelsrelated to the population excluding the client comprises all of thepopulation excluding the client.
 14. The method as claimed in claim 12wherein said models are codebooks each of a number of codewords.
 15. Themethod as claimed in claim 14 wherein said testing is effected byassessing a distance between centroids of pairs of the codebooks.
 16. Amethod as claimed in claim 14 wherein said testing is effected byassessing a distance between codewords in one said codebook andneighbour codewords in another said codebook.
 17. The method as claimedin claim 15 wherein the distance is a Euclidean distance.
 18. The methodof claim 16 wherein the distance is a Euclidean distance.
 19. The methodof claim 14 wherein the new model is generated by adding or subtractinga fixed vector displacement to the codewords of models in the populationexcluding any generated models.
 20. The method of claim 12 wherein themodels are vector quantization or hidden Markov models.
 21. The methodof claim 12 wherein said models represent speech characteristics. 22.The method of claim 12, further comprising comparing a model relating tosaid person with said cohort and determining whether the person is theclient on the basis of similarity of the models relating to the personand to the cohort.
 23. A method of assembling a cohort for a clientbeing one of a population, comprising testing whether models related tothe population excluding the client meet an acceptance threshold test asto identity with a model for the client, determining, from each meetingthe threshold test, whether those models are distributed so as topresent at least a given probability that models for non-members of thepopulation spaced from the client model in all directions will each becloser to a member of the cohort, excluding the client, than to theclient and, if that probability is less than a predetermined value,either selecting from the population another cohort member which willreduce that probability or generating a model for inclusion in thepopulation and which will reduce that probability.
 24. The method asclaimed in claim 23 wherein the models are vector quantisation or hiddenMarkov models.
 25. The method as claimed in claim 23 wherein said modelsrepresent speech characteristics.
 26. The method of claim 23, furthercomprising comparing a model relating to said person with said cohortand determining whether the person is the client on the basis ofsimilarity of the models relating to the person and to the cohort.